


import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 							lowerCamelCase__						(				UpperCAmelCase       ,						unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Optional[int] 			=		AudioLDMPipeline
			_UpperCamelCase		:       int 			=		TEXT_TO_AUDIO_PARAMS
			_UpperCamelCase		:       Any 			=		TEXT_TO_AUDIO_BATCH_PARAMS
			_UpperCamelCase		:       str 			=		frozenset(
			    [
			        'num_inference_steps',
			        'num_waveforms_per_prompt',
			        'generator',
			        'latents',
			        'output_type',
			        'return_dict',
			        'callback',
			        'callback_steps',
			    ]   )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						torch.manual_seed(0  )
						UpperCamelCase__       =							UNetaDConditionModel(
						    block_out_channels=(32, 64)   ,    layers_per_block=2   ,    sample_size=32   ,    in_channels=4   ,    out_channels=4   ,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   ,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")   ,    cross_attention_dim=(32, 64)   ,    class_embed_type="simple_projection"   ,    projection_class_embeddings_input_dim=32   ,    class_embeddings_concat=snake_case   ,    )
						UpperCamelCase__       =							DDIMScheduler(
						    beta_start=0.00085   ,    beta_end=0.012   ,    beta_schedule="scaled_linear"   ,    clip_sample=snake_case   ,    set_alpha_to_one=snake_case   ,    )
						torch.manual_seed(0  )
						UpperCamelCase__       =							AutoencoderKL(
						    block_out_channels=[32, 64]   ,    in_channels=1   ,    out_channels=1   ,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]   ,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]   ,    latent_channels=4   ,    )
						torch.manual_seed(0  )
						UpperCamelCase__       =							ClapTextConfig(
						    bos_token_id=0   ,    eos_token_id=2   ,    hidden_size=32   ,    intermediate_size=37   ,    layer_norm_eps=1E-0_5   ,    num_attention_heads=4   ,    num_hidden_layers=5   ,    pad_token_id=1   ,    vocab_size=1000   ,    projection_dim=32   ,    )
						UpperCamelCase__       =							ClapTextModelWithProjection(snake_case  )
						UpperCamelCase__       =							RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta"   ,    model_max_length=77  )

						UpperCamelCase__       =							SpeechTaHifiGanConfig(
						    model_in_dim=8   ,    sampling_rate=16000   ,    upsample_initial_channel=16   ,    upsample_rates=[2, 2]   ,    upsample_kernel_sizes=[4, 4]   ,    resblock_kernel_sizes=[3, 7]   ,    resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]   ,    normalize_before=snake_case   ,    )

						UpperCamelCase__       =							SpeechTaHifiGan(snake_case  )

						UpperCamelCase__       =							{
						    "unet": unet,
						    "scheduler": scheduler,
						    "vae": vae,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "vocoder": vocoder,
						}
						return components




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case=0  ):

						'''simple docstring'''


						if str(snake_case  ).startswith("mps"  ):
									UpperCamelCase__       =							torch.manual_seed(snake_case  )
						else:
									UpperCamelCase__       =							torch.Generator(device=snake_case  ).manual_seed(snake_case  )
						UpperCamelCase__       =							{
						    "prompt": "A hammer hitting a wooden surface",
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						}
						return inputs




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							"cpu"  # ensure determinism for the device-dependent torch.Generator

						UpperCamelCase__       =							self.get_dummy_components()
						UpperCamelCase__       =							AudioLDMPipeline(**snake_case  )
						UpperCamelCase__       =							audioldm_pipe.to(snake_case  )
						audioldm_pipe.set_progress_bar_config(disable=snake_case  )

						UpperCamelCase__       =							self.get_dummy_inputs(snake_case  )
						UpperCamelCase__       =							audioldm_pipe(**snake_case  )
						UpperCamelCase__       =							output.audios[0]

						assert audio.ndim == 1
						assert len(snake_case  ) == 256

						UpperCamelCase__       =							audio[:10]
						UpperCamelCase__       =							np.array(
						    [-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033]  )

						assert np.abs(audio_slice - expected_slice  ).max() < 1E-2




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_dummy_components()
						UpperCamelCase__       =							AudioLDMPipeline(**snake_case  )
						UpperCamelCase__       =							audioldm_pipe.to(snake_case  )
						UpperCamelCase__       =							audioldm_pipe.to(snake_case  )
						audioldm_pipe.set_progress_bar_config(disable=snake_case  )

						UpperCamelCase__       =							self.get_dummy_inputs(snake_case  )
						UpperCamelCase__       =							3 * [inputs["prompt"]]

						# forward
						UpperCamelCase__       =							audioldm_pipe(**snake_case  )
						UpperCamelCase__       =							output.audios[0]

						UpperCamelCase__       =							self.get_dummy_inputs(snake_case  )
						UpperCamelCase__       =							3 * [inputs.pop("prompt"  )]

						UpperCamelCase__       =							audioldm_pipe.tokenizer(
						    snake_case   ,    padding="max_length"   ,    max_length=audioldm_pipe.tokenizer.model_max_length   ,    truncation=snake_case   ,    return_tensors="pt"   ,    )
						UpperCamelCase__       =							text_inputs["input_ids"].to(snake_case  )

						UpperCamelCase__       =							audioldm_pipe.text_encoder(
						    snake_case   ,    )
						UpperCamelCase__       =							prompt_embeds.text_embeds
						# additional L_2 normalization over each hidden-state
						UpperCamelCase__       =							F.normalize(snake_case   ,    dim=-1  )

						UpperCamelCase__       =							prompt_embeds

						# forward
						UpperCamelCase__       =							audioldm_pipe(**snake_case  )
						UpperCamelCase__       =							output.audios[0]

						assert np.abs(audio_a - audio_a  ).max() < 1E-2




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_dummy_components()
						UpperCamelCase__       =							AudioLDMPipeline(**snake_case  )
						UpperCamelCase__       =							audioldm_pipe.to(snake_case  )
						UpperCamelCase__       =							audioldm_pipe.to(snake_case  )
						audioldm_pipe.set_progress_bar_config(disable=snake_case  )

						UpperCamelCase__       =							self.get_dummy_inputs(snake_case  )
						UpperCamelCase__       =							3 * ["this is a negative prompt"]
						UpperCamelCase__       =							negative_prompt
						UpperCamelCase__       =							3 * [inputs["prompt"]]

						# forward
						UpperCamelCase__       =							audioldm_pipe(**snake_case  )
						UpperCamelCase__       =							output.audios[0]

						UpperCamelCase__       =							self.get_dummy_inputs(snake_case  )
						UpperCamelCase__       =							3 * [inputs.pop("prompt"  )]

						UpperCamelCase__       =							[]
						for p in [prompt, negative_prompt]:
									UpperCamelCase__       =							audioldm_pipe.tokenizer(
									    snake_case   ,    padding="max_length"   ,    max_length=audioldm_pipe.tokenizer.model_max_length   ,    truncation=snake_case   ,    return_tensors="pt"   ,    )
									UpperCamelCase__       =							text_inputs["input_ids"].to(snake_case  )

									UpperCamelCase__       =							audioldm_pipe.text_encoder(
									    snake_case   ,    )
									UpperCamelCase__       =							text_embeds.text_embeds
									# additional L_2 normalization over each hidden-state
									UpperCamelCase__       =							F.normalize(snake_case   ,    dim=-1  )

									embeds.append(snake_case  )

						UpperCamelCase__,   UpperCamelCase__       =							embeds

						# forward
						UpperCamelCase__       =							audioldm_pipe(**snake_case  )
						UpperCamelCase__       =							output.audios[0]

						assert np.abs(audio_a - audio_a  ).max() < 1E-2




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							"cpu"  # ensure determinism for the device-dependent torch.Generator
						UpperCamelCase__       =							self.get_dummy_components()
						UpperCamelCase__       =							PNDMScheduler(skip_prk_steps=snake_case  )
						UpperCamelCase__       =							AudioLDMPipeline(**snake_case  )
						UpperCamelCase__       =							audioldm_pipe.to(snake_case  )
						audioldm_pipe.set_progress_bar_config(disable=snake_case  )

						UpperCamelCase__       =							self.get_dummy_inputs(snake_case  )
						UpperCamelCase__       =							"egg cracking"
						UpperCamelCase__       =							audioldm_pipe(**snake_case   ,    negative_prompt=snake_case  )
						UpperCamelCase__       =							output.audios[0]

						assert audio.ndim == 1
						assert len(snake_case  ) == 256

						UpperCamelCase__       =							audio[:10]
						UpperCamelCase__       =							np.array(
						    [-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032]  )

						assert np.abs(audio_slice - expected_slice  ).max() < 1E-2




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							"cpu"  # ensure determinism for the device-dependent torch.Generator
						UpperCamelCase__       =							self.get_dummy_components()
						UpperCamelCase__       =							PNDMScheduler(skip_prk_steps=snake_case  )
						UpperCamelCase__       =							AudioLDMPipeline(**snake_case  )
						UpperCamelCase__       =							audioldm_pipe.to(snake_case  )
						audioldm_pipe.set_progress_bar_config(disable=snake_case  )

						UpperCamelCase__       =							"A hammer hitting a wooden surface"

						# test num_waveforms_per_prompt=1 (default)
						UpperCamelCase__       =							audioldm_pipe(snake_case   ,    num_inference_steps=2  ).audios

						assert audios.shape == (1, 256)

						# test num_waveforms_per_prompt=1 (default) for batch of prompts
						UpperCamelCase__       =							2
						UpperCamelCase__       =							audioldm_pipe([prompt] * batch_size   ,    num_inference_steps=2  ).audios

						assert audios.shape == (batch_size, 256)

						# test num_waveforms_per_prompt for single prompt
						UpperCamelCase__       =							2
						UpperCamelCase__       =							audioldm_pipe(snake_case   ,    num_inference_steps=2   ,    num_waveforms_per_prompt=snake_case  ).audios

						assert audios.shape == (num_waveforms_per_prompt, 256)

						# test num_waveforms_per_prompt for batch of prompts
						UpperCamelCase__       =							2
						UpperCamelCase__       =							audioldm_pipe(
						    [prompt] * batch_size   ,    num_inference_steps=2   ,    num_waveforms_per_prompt=snake_case  ).audios

						assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							"cpu"  # ensure determinism for the device-dependent torch.Generator
						UpperCamelCase__       =							self.get_dummy_components()
						UpperCamelCase__       =							AudioLDMPipeline(**snake_case  )
						UpperCamelCase__       =							audioldm_pipe.to(snake_case  )
						audioldm_pipe.set_progress_bar_config(disable=snake_case  )
						UpperCamelCase__       =							audioldm_pipe.vocoder.config.sampling_rate

						UpperCamelCase__       =							self.get_dummy_inputs(snake_case  )
						UpperCamelCase__       =							audioldm_pipe(audio_length_in_s=0.016   ,    **snake_case  )
						UpperCamelCase__       =							output.audios[0]

						assert audio.ndim == 1
						assert len(snake_case  ) / vocoder_sampling_rate == 0.016

						UpperCamelCase__       =							audioldm_pipe(audio_length_in_s=0.032   ,    **snake_case  )
						UpperCamelCase__       =							output.audios[0]

						assert audio.ndim == 1
						assert len(snake_case  ) / vocoder_sampling_rate == 0.032




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_dummy_components()
						UpperCamelCase__       =							AudioLDMPipeline(**snake_case  )
						UpperCamelCase__       =							audioldm_pipe.to(snake_case  )
						audioldm_pipe.set_progress_bar_config(disable=snake_case  )

						UpperCamelCase__       =							["hey"]

						UpperCamelCase__       =							audioldm_pipe(snake_case   ,    num_inference_steps=1  )
						UpperCamelCase__       =							output.audios.shape
						assert audio_shape == (1, 256)

						UpperCamelCase__       =							audioldm_pipe.vocoder.config
						config.model_in_dim *= 2
						UpperCamelCase__       =							SpeechTaHifiGan(snake_case  ).to(snake_case  )
						UpperCamelCase__       =							audioldm_pipe(snake_case   ,    num_inference_steps=1  )
						UpperCamelCase__       =							output.audios.shape
						# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
						assert audio_shape == (1, 256)




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self._test_attention_slicing_forward_pass(test_mean_pixel_difference=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self._test_inference_batch_single_identical(test_mean_pixel_difference=snake_case  )







			@unittest.skipIf(
			    torch_device != "cuda" or not is_xformers_available()   ,    reason="XFormers attention is only available with CUDA and `xformers` installed"   ,    )
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=snake_case  )







@slow
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case="cpu"   ,    snake_case=torch.floataa   ,    snake_case=0  ):

						'''simple docstring'''


						UpperCamelCase__       =							torch.Generator(device=snake_case  ).manual_seed(snake_case  )
						UpperCamelCase__       =							np.random.RandomState(snake_case  ).standard_normal((1, 8, 128, 16)  )
						UpperCamelCase__       =							torch.from_numpy(snake_case  ).to(device=snake_case   ,    dtype=snake_case  )
						UpperCamelCase__       =							{
						    "prompt": "A hammer hitting a wooden surface",
						    "latents": latents,
						    "generator": generator,
						    "num_inference_steps": 3,
						    "guidance_scale": 2.5,
						}
						return inputs




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							AudioLDMPipeline.from_pretrained("cvssp/audioldm"  )
						UpperCamelCase__       =							audioldm_pipe.to(snake_case  )
						audioldm_pipe.set_progress_bar_config(disable=snake_case  )

						UpperCamelCase__       =							self.get_inputs(snake_case  )
						UpperCamelCase__       =							25
						UpperCamelCase__       =							audioldm_pipe(**snake_case  ).audios[0]

						assert audio.ndim == 1
						assert len(snake_case  ) == 81920

						UpperCamelCase__       =							audio[77230:77240]
						UpperCamelCase__       =							np.array(
						    [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]  )
						UpperCamelCase__       =							np.abs(expected_slice - audio_slice  ).max()
						assert max_diff < 1E-2







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							AudioLDMPipeline.from_pretrained("cvssp/audioldm"  )
						UpperCamelCase__       =							LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config  )
						UpperCamelCase__       =							audioldm_pipe.to(snake_case  )
						audioldm_pipe.set_progress_bar_config(disable=snake_case  )

						UpperCamelCase__       =							self.get_inputs(snake_case  )
						UpperCamelCase__       =							audioldm_pipe(**snake_case  ).audios[0]

						assert audio.ndim == 1
						assert len(snake_case  ) == 81920

						UpperCamelCase__       =							audio[27780:27790]
						UpperCamelCase__       =							np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]  )
						UpperCamelCase__       =							np.abs(expected_slice - audio_slice  ).max()
						assert max_diff < 3E-2






from unittest.mock import Mock, patch

from file_transfer.send_file import send_file
@patch("socket.socket"	)
@patch("builtins.open"	)
def 							UpperCamelCase_(					_A :Tuple					,  _A :str	)->		int:
			# ===== initialization =====
			UpperCamelCase__       =							Mock()
			UpperCamelCase__       =							conn, Mock()
			UpperCamelCase__       =							iter([1, None]	)
			UpperCamelCase__       =							lambda _A	: next(_A	)

			# ===== invoke =====
			send_file(filename="mytext.txt"					,  testing=_A	)

			# ===== ensurance =====
			sock.assert_called_once()
			sock.return_value.bind.assert_called_once()
			sock.return_value.listen.assert_called_once()
			sock.return_value.accept.assert_called_once()
			conn.recv.assert_called_once()

			file.return_value.__enter__.assert_called_once()
			file.return_value.__enter__.return_value.read.assert_called()

			conn.send.assert_called_once()
			conn.close.assert_called_once()
			sock.return_value.shutdown.assert_called_once()
			sock.return_value.close.assert_called_once()


